{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a data set for analysis\n",
    "x, y = make_regression(n_samples=500, n_features = 1, noise=25, random_state=0)\n",
    "y = np.exp((y + abs(y.min())) / 75)\n",
    "\n",
    "# Split the data set into testing and training data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)\n",
    "\n",
    "# Plot the data\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.regplot(x_test, y_test, fit_reg=False)\n",
    "\n",
    "# Remove ticks from the plot\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
